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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Action Recognition using Key-Frame Features of Depth Sequence and ELM

Author 1: Suolan Liu Author 2: Hongyuan Wang
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 8, No. 10 · Published 2017

DOI: https://doi.org/10.14569/IJACSA.2017.081007

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Abstract

Recently, the rapid development of inexpensive RGB-D sensor, like Microsoft Kinect, provides adequate information for human action recognition. In this paper, a recognition algorithm is presented in which feature representation is generated by concatenating spatial features from human contour of key frames and temporal features from time difference information of a sequence. Then, an improved multi-hidden layers extreme learning machine is introduced as classifier. At last, we test our scheme on the public UTD-MHAD dataset from recognition accuracy and time consumption.

Keywords

Action recognition features key frame temporal extreme learning machine

How to Cite this Article

Liu, S., & Wang, H. (2017). Action Recognition using Key-Frame Features of Depth Sequence and ELM. International Journal of Advanced Computer Science and Applications, 8(10). https://doi.org/10.14569/IJACSA.2017.081007

Liu, Suolan, and Hongyuan Wang. "Action Recognition using Key-Frame Features of Depth Sequence and ELM." International Journal of Advanced Computer Science and Applications, vol. 8, no. 10, 2017, https://doi.org/10.14569/IJACSA.2017.081007.

@article{Liu2017,
  title     = {Action Recognition using Key-Frame Features of Depth Sequence and ELM},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {10},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Suolan Liu and Hongyuan Wang},
  doi       = {10.14569/IJACSA.2017.081007},
  url       = {https://doi.org/10.14569/IJACSA.2017.081007}
}

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